Constraint-based knowledge discovery from SAGE data.
Current analyses of co-expressed genes are often based on global approaches such as clustering or bi-clustering. An alternative way is to employ local methods and search for patterns--sets of genes displaying specific expression properties in a set of situations. The main bottleneck of this type of analysis is twofold--computational costs and an overwhelming number of candidate patterns which can hardly be further exploited. A timely application of background knowledge available in literature databases, biological ontologies and other sources can help to focus on the most plausible patterns only. The paper proposes, implements and tests a flexible constraint-based framework that enables the effective mining and representation of meaningful over-expression patterns representing intrinsic associations among genes and biological situations. The framework can be simultaneously applied to a wide spectrum of genomic data and we demonstrate that it allows to generate new biological hypotheses with clinical implications.
Klémal
Jirí
J
GREYC, CNRS UMR 6072, Université de Caen, Campus Côte de Nacre, F-14032 Caen Cédex, France.
Blachon
Sylvain
S
Soulet
Arnaud
A
Crémilleux
Bruno
B
Gandrillon
Olivier
O
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods; D023281:Genomics / Q000379:methods; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software